library(GenomicAlignments)
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library(tidyverse)
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library(cqn)
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library(edgeR)
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library(ggplot2)
library(cowplot)
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## theme_set(theme_cowplot())
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library(ggridges)
library(gridExtra)
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## combine
colsBig <- clusterExperiment:::massivePalette
plotGCHex <- function(gr, counts){
counts2 <- counts
df <- as_tibble(cbind(counts2,gc=mcols(gr)$gc))
df <- gather(df, sample, value, -gc)
ggplot(data=df, aes(x=gc, y=log(value+1)) ) +
ylab("log(count + 1)") + xlab("GC-content") +
geom_hex(bins = 50) + theme_bw() #+ facet_wrap(~sample, nrow=2)
}
pal <- RColorBrewer::brewer.pal(n=8, "Dark2")
source("../../methods/gcqn_validated.R")
data=read.delim("../../data/rizzardi2019_GSE96614/GSE96614_flow_sorted_brain.ATAC-seq_counts.txt.gz")
# get GC content
sn <- gsub(x=data[,1], pattern="chr", replacement="")
start <- data[,2]
end <- data[,3]
gr <- GRanges(seqnames=sn, ranges=IRanges(start, end), strand="*")
ff <- FaFile("~/data/genomes/human/Homo_sapiens.GRCh37.75.dna_sm.primary_assembly.fa.gz")
peakSeqs <- getSeq(x=ff, gr)
gcContentPeaks <- letterFrequency(peakSeqs, "GC",as.prob=TRUE)[,1]
mcols(gr)$gc <- gcContentPeaks
gcGroups <- Hmisc::cut2(gcContentPeaks, g=20)
counts <- as.matrix(data[,-c(1:3)])
rownames(counts) <- 1:nrow(counts)
# get metadata
individual <- as.factor(substr(colnames(counts),2,5))
region <- as.factor(unlist(lapply(strsplit(colnames(counts), split=".", fixed=TRUE), "[[", 2)))
neuCell <- as.factor(unlist(lapply(strsplit(colnames(counts), split=".", fixed=TRUE), "[[", 3)))
table(neuCell, region, individual)
## , , individual = 5343
##
## region
## neuCell BA9 NA
## neg 1 1
## pos 1 1
##
## , , individual = 5347
##
## region
## neuCell BA9 NA
## neg 1 1
## pos 1 1
##
## , , individual = 5358
##
## region
## neuCell BA9 NA
## neg 1 1
## pos 1 1
##
## , , individual = 5404
##
## region
## neuCell BA9 NA
## neg 1 0
## pos 1 0
##
## , , individual = 5456
##
## region
## neuCell BA9 NA
## neg 1 1
## pos 1 1
##
## , , individual = 5540
##
## region
## neuCell BA9 NA
## neg 1 1
## pos 1 1
condition <- factor(paste0(neuCell, region))
table(condition)
## condition
## negBA9 negNA posBA9 posNA
## 6 5 6 5
design <- model.matrix(~condition+individual)
# GC content effect across entire dataset
p1 <- plotGCHex(gr, rowSums(counts)) +
theme(axis.title = element_text(size=16)) +
labs(fill="Nr. of peaks")
p1
lowListGC <- list()
for(kk in 1:ncol(counts)){
set.seed(kk)
lowListGC[[kk]] <- lowess(x=gcContentPeaks, y=log1p(counts[,kk]), f=1/10)
}
for(cc in 1:nlevels(condition)){
curCT <- levels(condition)[cc]
id <- which(condition == curCT)
plot(x=seq(min(gcContentPeaks), max(gcContentPeaks), length=10),
y=seq(2, 7, length=10), type='n',
xlab="GC-content", ylab="log(count + 1)", main=curCT)
for(ii in 1:length(id)){
curID <- id[ii]
oo <- order(lowListGC[[curID]]$x)
lines(x=lowListGC[[curID]]$x[oo], y=lowListGC[[curID]]$y[oo], col=colsBig[ii])
}
}
negBA9 <- lowListGC[condition == "negBA9"]
dfList <- list()
for(ss in 1:length(negBA9)){
oox <- order(negBA9[[ss]]$x)
dfList[[ss]] <- data.frame(x=negBA9[[ss]]$x[oox], y=negBA9[[ss]]$y[oox], sample=ss)
}
dfAll <- do.call(rbind, dfList)
dfAll$sample <- factor(dfAll$sample)
## association of GC content with counts
plotGCHex(gr, rowMeans(counts[, condition == "negBA9"])) +
theme(axis.title = element_text(size=16)) +
labs(fill="Nr. of peaks") +
geom_line(aes(x=x, y=y, group=sample, color=sample), data=dfAll, size=1) +
scale_color_discrete()
## just the average GC content
p1 <- ggplot(dfAll, aes(x=x, y=y, group=sample, color=sample)) +
geom_line(size = 1) +
xlab("GC-content") +
ylab("log(count + 1)") +
theme_classic()
p1
# across all conditions
set.seed(44)
pList <- c()
id <- sample(nrow(counts), size=1e4)
for(cc in 1:nlevels(condition)){
curCT <- levels(condition)[cc]
lowCT <- lowListGC[condition == curCT]
dfList <- list()
for(ss in 1:length(lowCT)){
oox <- order(lowCT[[ss]]$x[id])
dfList[[ss]] <- data.frame(x=lowCT[[ss]]$x[id][oox], y=lowCT[[ss]]$y[id][oox], sample=ss)
}
dfAll <- do.call(rbind, dfList)
dfAll$sample <- factor(dfAll$sample)
pCT <- ggplot(dfAll, aes(x=x, y=y, group=sample, color=sample)) +
geom_line(size = 1) +
xlab("GC-content") +
ylab("log(count + 1)") +
theme_classic() +
ggtitle(curCT) +
theme(legend.position = "none") +
ylim(c(3, 7))
pList[[cc]] <- pCT
}
cowplot::plot_grid(plotlist=pList, nrow=4, ncol=4)
## Warning: Removed 9490 row(s) containing missing values (geom_path).
## Warning: Removed 1228 row(s) containing missing values (geom_path).
## Warning: Removed 9175 row(s) containing missing values (geom_path).
## Warning: Removed 28540 row(s) containing missing values (geom_path).
rm(lowListGC, lowCT, pList) ; gc()
## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 9215708 492.2 17771204 949.1 NA 17771204 949.1
## Vcells 219260982 1672.9 612884016 4676.0 102400 765032315 5836.8
lowListWidth <- list()
for(kk in 1:ncol(counts)){
lowListWidth[[kk]] <- lowess(x=log(width(gr)), y=log1p(counts[,kk]), f=1/10)
}
plot(x=seq(min(log(width(gr))), max(log(width(gr))), length=10),
y=seq(0, 5, length=10), type='n',
xlab="GC-content", ylab="log(count + 1)")
for(kk in 1:length(lowListWidth)){
oo <- order(lowListWidth[[kk]]$x)
lines(x=lowListWidth[[kk]]$x[oo], y=lowListWidth[[kk]]$y[oo], col=colsBig[kk])
}
# across all cell types
set.seed(44)
pList <- c()
id <- sample(nrow(counts), size=1e4)
for(cc in 1:nlevels(condition)){
curCT <- levels(condition)[cc]
lowCT <- lowListWidth[condition == curCT]
dfList <- list()
for(ss in 1:length(lowCT)){
oox <- order(lowCT[[ss]]$x[id])
dfList[[ss]] <- data.frame(x=lowCT[[ss]]$x[id][oox], y=lowCT[[ss]]$y[id][oox], sample=ss)
}
dfAll <- do.call(rbind, dfList)
dfAll$sample <- factor(dfAll$sample)
pCT <- ggplot(dfAll, aes(x=x, y=y, group=sample, color=sample)) +
geom_line(size = 1) +
xlab("Log peak width") +
ylab("log(count + 1)") +
theme_classic() +
ggtitle(curCT) +
theme(legend.position = "none") +
ylim(c(2.5, 8.5))
pList[[cc]] <- pCT
}
cowplot::plot_grid(plotlist=pList, nrow=4, ncol=4)
## Warning: Removed 7538 row(s) containing missing values (geom_path).
## Warning: Removed 15121 row(s) containing missing values (geom_path).
rm(lowListWidth) ; gc()
## used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
## Ncells 9096425 485.9 17771204 949.1 NA 17771204 949.1
## Vcells 241681678 1843.9 612884016 4676.0 102400 765032315 5836.8
Note that the comparisons here aren’t mock comparisons; they compare biologically different groups.
sampleID <- condition == "negBA9"
counts <- counts[,sampleID]
set.seed(21)
mock <- factor(sample(rep(1:2, each=3)))
designMock <- model.matrix(~mock)
## TMM normalization
library(edgeR)
d <- DGEList(counts)
d <- calcNormFactors(d)
d <- estimateDisp(d, designMock)
fit <- glmFit(d, designMock)
lrt <- glmLRT(fit, coef=2)
dfEdgeR <- data.frame(logFC=log(2^lrt$table$logFC),
gc=gcGroups)
pedgeR <- ggplot(dfEdgeR) +
aes(x=gc, y=logFC, color=gc) +
geom_violin() +
geom_boxplot(width=0.1) +
scale_color_manual(values=wesanderson::wes_palette("Zissou1", nlevels(gcGroups), "continuous")) +
geom_abline(intercept = 0, slope = 0, col="black", lty=2) +
theme_bw() +
ylim(c(-1,1)) +
ggtitle("TMM normalization") +
xlab("GC-content bin") +
theme(axis.text.x = element_text(angle = 45, vjust = .5),
legend.position = "none",
axis.title = element_text(size=16)) +
geom_smooth(se=FALSE, color="blue", aes(group=1), lwd=1)
pedgeR
## Warning: Removed 141 rows containing non-finite values (stat_ydensity).
## Warning: Removed 141 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 141 rows containing non-finite values (stat_smooth).
## DESeq2 normalization
library(DESeq2)
dds <- DESeqDataSetFromMatrix(counts,
colData=data.frame(mock=mock),
design=~mock)
dds <- DESeq(dds)
## estimating size factors
## estimating dispersions
## gene-wise dispersion estimates
## mean-dispersion relationship
## final dispersion estimates
## fitting model and testing
res <- results(dds, name="mock_2_vs_1")
dfDESeq2 <- data.frame(logFC=log(2^res$log2FoldChange),
gc=gcGroups)
pdeseq <- ggplot(dfDESeq2) +
aes(x=gc, y=logFC, color=gc) +
geom_violin() +
geom_boxplot(width=0.1) +
scale_color_manual(values=wesanderson::wes_palette("Zissou1", nlevels(gcGroups), "continuous")) +
geom_abline(intercept = 0, slope = 0, col="black", lty=2) +
theme_bw() +
ylim(c(-1,1)) +
ggtitle("DESeq2 MOR normalization") +
xlab("GC-content bin") +
theme(axis.text.x = element_text(angle = 45, vjust = .5),
legend.position = "none",
axis.title = element_text(size=16)) +
geom_smooth(se=FALSE, color="blue", aes(group=1), lwd=1)
pdeseq
## Warning: Removed 145 rows containing non-finite values (stat_ydensity).
## Warning: Removed 145 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 145 rows containing non-finite values (stat_smooth).
## Full quantile normalization
countsFQ <- FQnorm(counts, type="median")
d <- DGEList(countsFQ)
d <- estimateDisp(d, designMock)
fit <- glmFit(d, designMock)
lrtFQ <- glmLRT(fit, coef=2)
dfFQ <- data.frame(logFC=log(2^lrtFQ$table$logFC),
gc=gcGroups)
pFQ <- ggplot(dfFQ) +
aes(x=gc, y=logFC, color=gc) +
geom_violin() +
geom_boxplot(width=0.1) +
scale_color_manual(values=wesanderson::wes_palette("Zissou1", nlevels(gcGroups), "continuous")) +
geom_abline(intercept = 0, slope = 0, col="black", lty=2) +
theme_bw() +
ylim(c(-1,1)) +
ggtitle("FQ normalization") +
xlab("GC-content bin") +
theme(axis.text.x = element_text(angle = 45, vjust = .5),
legend.position = "none",
axis.title = element_text(size=16)) +
geom_smooth(se=FALSE, color="blue", aes(group=1), lwd=1)
pFQ
## Warning: Removed 139 rows containing non-finite values (stat_ydensity).
## Warning: Removed 139 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 139 rows containing non-finite values (stat_smooth).
p <- plot_grid(p1 + ggtitle("negBA9 cells"),
pedgeR,
pdeseq,
pFQ,
labels=letters[1:4])
## Warning: Removed 141 rows containing non-finite values (stat_ydensity).
## Warning: Removed 141 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 141 rows containing non-finite values (stat_smooth).
## Warning: Removed 145 rows containing non-finite values (stat_ydensity).
## Warning: Removed 145 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 145 rows containing non-finite values (stat_smooth).
## Warning: Removed 139 rows containing non-finite values (stat_ydensity).
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## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 139 rows containing non-finite values (stat_smooth).
p
ggsave("~/Dropbox/research/atacseq/bulk/plots/figure1_rizzardi.png",
units="in", width=12, height=9)
## cqn
cqnModel <- cqn(counts, x=gcContentPeaks, sizeFactors = colSums(counts), lengthMethod="fixed", lengths = 1000)
## Warning: The use of 'sig2' is deprecated; do specify 'sigma' (= sqrt(sig2))
## instead
d <- DGEList(counts)
d$offset <- cqnModel$glm.offset
d <- estimateDisp(d, designMock)
fit <- glmFit(d, designMock)
lrtCqn <- glmLRT(fit, coef=2)
dfCqn <- data.frame(logFC=log(2^lrtCqn$table$logFC),
gc=gcGroups)
pCqn <- ggplot(dfCqn) +
aes(x=gc, y=logFC, color=gc) +
geom_violin() +
geom_boxplot(width=0.1) +
scale_color_manual(values=wesanderson::wes_palette("Zissou1", nlevels(gcGroups), "continuous")) +
geom_abline(intercept = 0, slope = 0, col="black", lty=2) +
theme_bw() +
ylim(c(-1,1)) +
xlab("GC-content bin") +
ggtitle("cqn normalization") +
theme(axis.text.x = element_text(angle = 45, vjust = .5),
legend.position = "none",
axis.title = element_text(size=16)) +
geom_smooth(se=FALSE, color="blue", aes(group=1), lwd=1)
pCqn
## Warning: Removed 344 rows containing non-finite values (stat_ydensity).
## Warning: Removed 344 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 344 rows containing non-finite values (stat_smooth).
# ## EDASeq
library(EDASeq)
## Loading required package: ShortRead
##
## Attaching package: 'ShortRead'
## The following object is masked from 'package:dplyr':
##
## id
## The following object is masked from 'package:purrr':
##
## compose
## The following object is masked from 'package:tibble':
##
## view
#emptyRows <- which(rownames(counts) == "")
#rownames(counts)[emptyRows] <- paste0("emptyPeak",1:length(emptyRows))
dataWithin <- withinLaneNormalization(counts, y=gcContentPeaks,
num.bins=20, which="full")
dataNorm <- betweenLaneNormalization(dataWithin, which="full")
d <- DGEList(dataNorm)
d <- estimateDisp(d, designMock)
## Warning: Zero sample variances detected, have been offset away from zero
fit <- glmFit(d, designMock)
lrtEDASeq <- glmLRT(fit, coef=2)
dfEDASeq <- data.frame(logFC=log(2^lrtEDASeq$table$logFC),
gc=gcGroups)
pEDASeq <- ggplot(dfEDASeq) +
aes(x=gc, y=logFC, color=gc) +
geom_violin() +
geom_boxplot(width=0.1) +
scale_color_manual(values=wesanderson::wes_palette("Zissou1", nlevels(gcGroups), "continuous")) +
geom_abline(intercept = 0, slope = 0, col="black", lty=2) +
theme_bw() +
ylim(c(-1,1)) +
xlab("GC-content bin") +
ggtitle("FQ-FQ normalization") +
theme(axis.text.x = element_text(angle = 45, vjust = .5),
legend.position = "none",
axis.title = element_text(size=16)) +
geom_smooth(se=FALSE, color="blue", aes(group=1), lwd=1)
## GC-QN
countsGCQN <- gcqn(counts, gcGroups, summary = "median")
d <- DGEList(countsGCQN)
d <- estimateDisp(d, designMock)
fit <- glmFit(d, designMock)
lrtGCQN <- glmLRT(fit, coef=2)
dfGCQN <- data.frame(logFC=log(2^lrtGCQN$table$logFC),
gc=gcGroups)
pGCQN <- ggplot(dfGCQN) +
aes(x=gc, y=logFC, color=gc) +
geom_violin() +
geom_boxplot(width=0.1) +
scale_color_manual(values=wesanderson::wes_palette("Zissou1", nlevels(gcGroups), "continuous")) +
geom_abline(intercept = 0, slope = 0, col="black", lty=2) +
theme_bw() +
ylim(c(-1,1)) +
xlab("GC-content bin") +
ggtitle("GC-FQ normalization") +
theme(axis.text.x = element_text(angle = 45, vjust = .5),
legend.position = "none",
axis.title = element_text(size=16)) +
geom_smooth(se=FALSE, color="blue", aes(group=1), lwd=1)
## ridges plot
countsN <- counts[,1:3]
gcGroups10 <- Hmisc::cut2(gcContentPeaks, g=10)
lc <- log1p(c(countsN))
joyDat <- data.frame(lc=lc,
gc=rep(gcGroups10, 3),
sample=rep(1:3, each=nrow(countsN)))
axText <- 0
pRidge1 <- joyDat %>% ggplot(aes(y=gc)) +
geom_density_ridges(aes(x=lc), bandwidth = 0.1) +
facet_wrap(.~sample, nrow=1) +
theme_ridges(grid=FALSE, font_size=5, center_axis_labels = TRUE) +
xlim(c(1.5,9)) +
xlab("log(count + 1)") +
ylab("GC-content bin") +
theme(axis.text.y = element_text(size=axText),
axis.text.x = element_text(size=10),
legend.position = "none",
axis.title = element_text(size=16),
strip.background = element_blank(),
strip.text.x = element_blank())
pFC <- cowplot::plot_grid(pCqn, pEDASeq, pGCQN,
labels=letters[2:4],
nrow=3, ncol=1)
## Warning: Removed 344 rows containing non-finite values (stat_ydensity).
## Warning: Removed 344 rows containing non-finite values (stat_boxplot).
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
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## Warning: Removed 198 rows containing non-finite values (stat_ydensity).
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## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 198 rows containing non-finite values (stat_smooth).
## Warning: Removed 159 rows containing non-finite values (stat_ydensity).
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## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
## Warning: Removed 159 rows containing non-finite values (stat_smooth).
pFig2 <- cowplot::plot_grid(pRidge1,
pFC,
labels=c("a",""))
## Warning: Removed 27139 rows containing non-finite values (stat_density_ridges).
pFig2
ggsave("~/Dropbox/research/atacseq/bulk/plots/figure2_rizzardi.png",
units="in", width=12, height=9)